Topological Learning for Brain Networks
Topological Learning for Brain Networks
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DOI：
10.1214/22-aoas1633
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Tananun Songdechakraiwut;M. Chung
中科院分区：
文献类型：
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作者：
Tananun Songdechakraiwut;M. Chung
本文提出了一种新的拓扑学习框架，可以集成不同规模和拓扑结构的网络，通过持久同源。这是可能的，通过引入一个新的拓扑损失函数，使这种具有挑战性的任务。所提出的损失函数的使用绕过了与匹配网络相关联的固有计算瓶颈。我们验证了广泛的统计模拟与地面实况的方法，以评估区分不同拓扑结构的网络的拓扑损失的有效性。该方法进一步应用于双胞胎脑成像研究，以确定大脑网络是否是遗传的。挑战在于将从静息态功能磁共振成像（fMRI）获得的拓扑结构不同的功能脑网络覆盖到通过扩散张量成像（DTI）获得的模板结构脑网络上。
This paper proposes a novel topological learning framework that can integrate networks of different sizes and topology through persistent homology. This is possible through the introduction of a new topological loss function that enables such challenging task. The use of the proposed loss function bypasses the intrinsic computational bottleneck associated with matching networks. We validate the method in extensive statistical simulations with ground truth to assess the effectiveness of the topological loss in discriminating networks with different topology. The method is further applied to a twin brain imaging study in determining if the brain network is genetically heritable. The challenge is in overlaying the topologically different functional brain networks obtained from the resting-state functional magnetic resonance imaging (fMRI) onto the template structural brain network obtained through the diffusion tensor imaging (DTI).